Abstract

Nowadays, there have been great advances in the location technology. The personal
positioning oers a very interesting eld of research because the user walking has an
unpredictable behaviour and it is dicult to assume predened routes or to take into
account other implemented location techniques for vehicles or robots.
An approach for integration between inertial navigation systems (INS) and GPS is
presented. GPS is a navigation aid accurate and reliable but susceptible to interference
like multipath. An INS is very accurate over short periods, but its errors drift unbounded
over time. Blending INS with GPS can remedy the performance issues of both.
GPS is often combined with other sensors like accelerometers, gyroscopes or magnetometers.
The data fusion from these sensors is very important because they allow us
to calculate the position and orientation constantly. In this project we are interested in
analysing the system behaviour when the signal GPS is unavailable as when the signal is
blocked or in indoor environments. The analysis will be carried out through the assessment
of a Dead Reckoning algorithm to improve the position information. The system
was tested both indoor and outdoor of the Thales building. The personal positioning
system is made up of: a receiver GPS, an electronic compass, and the IMU.
There are many types of integration methods, and sensors vary greatly, from the
complex and expensive, to the simple and inexpensive, in this project it has been used
low cost sensors in a loosely coupled approach.
A Kalman alter for closed loop integration between GPS and INS is done. The lter
propagates and estimates the error states, which are fed back to the INS for correction
of the internal navigation states. The integration algorithm has been implemented on
Matlab. The algorithm receives the GPS and inertial measurements via serial port to
later process all the data. The algorithm has been used to experimentally test and
compare navigation performance.